Genomic signatures to guide the use of chemotherapeutics

Abstract

Using in vitro drug sensitivity data coupled with Affymetrix microarray data, we developed gene expression signatures that predict sensitivity to individual chemotherapeutic drugs. Each signature was validated with response data from an independent set of cell line studies. We further show that many of these signatures can accurately predict clinical response in individuals treated with these drugs. Notably, signatures developed to predict response to individual agents, when combined, could also predict response to multidrug regimens. Finally, we integrated the chemotherapy response signatures with signatures of oncogenic pathway deregulation to identify new therapeutic strategies that make use of all available drugs. The development of gene expression profiles that can predict response to commonly used cytotoxic agents provides opportunities to better use these drugs, including using them in combination with existing targeted therapies.

Genome-wide DNA copy number predictors of lapatinib sensitivity in tumor-derived cell lines

“…First, it suggests that molecular profiling using genome-wide assays can be a powerful and valid means of identifying biomarkers predictive of clinical response. This has been shown even for less selective agents, such as molecular profiles that predict for taxane activity in the treatment of breast cancer (31). Second, genome-wide profiles could point to additional genetic aberrations that further influence the efficacy of an agent in the absence of (or in conjunction with) primary markers; the fact that clinical responses may occur in only f25% of an already-enriched population (32) highlights the potential existence of additional predictive markers.…”

Optimization of drug combinations using Feedback System Control

“…(ii) Statistical approaches-linear combinations of known input-output relations and desired phenotype are developed, neglecting the nonlinearity in biological networks. In these models, the system is treated as a black box, and it does not require a complete characterization of the biological networks 37,38 . (iii) Model-based combinations in which biological measurements are used to build explicit models of a target network using simulations 39,40 .…”

A mitotically active, cellular tumor stroma and/or inflammatory cells associated with tumor cells may contribute to intermediate or high Oncotype DX Recurrence Scores in low-grade invasive breast carcinomas

“…38 The currently most widely used such assay in the United States is Oncotype DX (Genomic Health, Redwood City, CA, USA), an RT-PCR-based assay, which analyzes 16 cancer-related and 5 reference genes to provide a Recurrence Score. The Recurrence Score is based on the assumption that patients will receive adjuvant hormonal manipulation and predicts a 10-year distant recurrence risk as a continuous variable.…”